An image processing method includes: determining a brightness value of an image; and enhancing a brightness of the image when the brightness value of the image is less than an image brightness threshold. Enhancing the brightness of the image includes: determining each pixel of the image as a target pixel; determining a brightness enhancement value of the target pixel based on the brightness value of the image, an initial brightness value of the target pixel, and initial brightness values of neighboring pixels of the target pixel; and using the brightness enhancement value as an enhanced brightness value of the target pixel.
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5. The method according to claim 4, wherein the sampling interval comprises a first interval component in a row direction and a second interval component in a column direction of the pixels of the image, the first interval component being a value obtained by dividing a quantity of pixels in the row direction by the sampling rate, and the second interval component being a value obtained by dividing a quantity of pixels in the column direction by the sampling rate.
This invention relates to image processing, specifically to methods for sampling pixels in an image to reduce data volume while preserving key features. The problem addressed is efficiently reducing the number of pixels processed or stored without losing critical information, which is important for applications like real-time image analysis, compression, or machine learning. The method involves sampling pixels from an image using a sampling rate and a sampling interval. The sampling interval is defined by two components: one for the row direction (horizontal) and one for the column direction (vertical). The row interval is calculated by dividing the total number of pixels in the row direction by the sampling rate. Similarly, the column interval is calculated by dividing the total number of pixels in the column direction by the same sampling rate. This creates a grid-like pattern where pixels are selected at regular intervals in both directions, ensuring uniform coverage across the image. The sampling rate determines how densely or sparsely pixels are selected. A higher sampling rate results in more pixels being retained, while a lower rate reduces the number of pixels. The method ensures that the sampling process is consistent and predictable, allowing for efficient processing while maintaining structural integrity of the image. This approach is useful in applications where computational efficiency is critical, such as video processing, medical imaging, or autonomous systems.
7. The method according to claim 1, wherein the neighboring pixels of the target pixel comprise other pixels in a region with the target pixel as a central point.
This invention relates to image processing, specifically to methods for analyzing pixel neighborhoods in digital images. The problem addressed is efficiently determining relationships between a target pixel and its neighboring pixels to improve image analysis tasks such as noise reduction, edge detection, or feature extraction. The method involves selecting a target pixel in a digital image and identifying neighboring pixels within a defined region centered on the target pixel. The neighboring pixels are those located within a specified distance or pattern around the target pixel, forming a local neighborhood for analysis. The method may further include analyzing the pixel values or attributes of these neighboring pixels to derive information about the target pixel, such as its brightness, color, or texture. This analysis can be used to enhance image quality, detect edges, or classify regions within the image. The method may also involve applying filters or transformations to the neighboring pixels to reduce noise, sharpen edges, or extract features. The size and shape of the neighborhood region can be adjusted based on the specific application or image characteristics. By focusing on local pixel relationships, the method improves the accuracy and efficiency of image processing tasks compared to global or random sampling approaches.
12. The apparatus according to claim 11, wherein the processor is further configured to: sample pixels of the image at a sampling interval to obtain brightness components in YUV color space data of the sampled pixels; add the brightness components of the sampled pixels to obtain a sum of brightness of the sampled pixels; perform sampling rate smoothing on the sum of the brightness of the sampled pixels to obtain a smoothed brightness value; and determine the smoothed brightness value as the brightness value of the image.
This invention relates to image processing, specifically to determining the brightness value of an image. The problem addressed is accurately measuring image brightness while reducing computational complexity and noise. The apparatus includes a processor configured to sample pixels of an image at a defined interval to extract brightness components from YUV color space data. The brightness components of these sampled pixels are summed to produce a total brightness value. To improve accuracy and reduce noise, the sum undergoes sampling rate smoothing, resulting in a smoothed brightness value. This smoothed value is then designated as the final brightness measurement for the image. The method ensures efficient brightness calculation by leveraging YUV color space data, which separates luminance (brightness) from chrominance (color), allowing for precise brightness assessment without processing full-color information. The sampling and smoothing steps further optimize performance by reducing the number of computations while maintaining accuracy. This approach is particularly useful in applications requiring real-time brightness analysis, such as automatic exposure control in cameras or image enhancement algorithms.
13. The apparatus according to claim 12, wherein the sampling interval comprises a first interval component in a row direction and a second interval component in a column direction of the pixels of the image, the first interval component being a value obtained by dividing a quantity of pixels in the row direction by the sampling rate, and the second interval component being a value obtained by dividing a quantity of pixels in the column direction by the sampling rate.
This invention relates to image processing, specifically to an apparatus for sampling pixels in an image to reduce data volume while preserving key information. The problem addressed is the need to efficiently sample pixels in both row and column directions of an image to achieve a desired sampling rate without losing critical spatial details. The apparatus includes a sampling module that selects pixels from an image based on a sampling interval. The sampling interval is defined by two components: a first interval in the row direction and a second interval in the column direction. The first interval is calculated by dividing the total number of pixels in the row direction by the sampling rate. Similarly, the second interval is calculated by dividing the total number of pixels in the column direction by the same sampling rate. This ensures uniform sampling across both dimensions, maintaining spatial consistency. The sampling rate determines how many pixels are skipped between selected pixels. For example, a sampling rate of 2 means every second pixel is selected in both row and column directions. The apparatus dynamically adjusts the sampling intervals based on the image dimensions and the desired sampling rate, allowing flexible adaptation to different image sizes and resolutions. This method reduces computational load and storage requirements while preserving the structural integrity of the image.
14. The apparatus according to claim 12, wherein the processor is further configured to: divide the sum of the brightness of the sampled pixels by a square of the sampling rate to obtain the smoothed brightness value.
This invention relates to image processing, specifically to a method for calculating a smoothed brightness value from sampled pixels in an image. The problem addressed is accurately determining brightness while reducing noise and computational complexity in digital imaging systems. The apparatus includes a processor configured to sample pixels from an image and calculate a brightness value for each sampled pixel. The processor then sums the brightness values of the sampled pixels. To obtain a smoothed brightness value, the processor divides this sum by the square of the sampling rate. This mathematical operation normalizes the brightness value based on the sampling density, improving accuracy and reducing artifacts caused by irregular sampling patterns. The sampling rate is defined as the number of pixels sampled per unit area or along a given dimension. By dividing the summed brightness by the square of this rate, the processor compensates for variations in sampling density, ensuring consistent brightness measurements regardless of sampling frequency. This technique is particularly useful in applications requiring high dynamic range or low-light imaging, where noise and sampling artifacts can degrade image quality. The invention improves upon prior methods by providing a computationally efficient way to smooth brightness values while maintaining accuracy. The use of the sampling rate squared ensures proper normalization, making the method adaptable to different imaging systems and sampling strategies. This approach is applicable in digital cameras, medical imaging, and other fields where precise brightness measurement is critical.
15. The apparatus according to claim 10, wherein the neighboring pixels of the target pixel comprise other pixels in a region with the target pixel as a central point.
This invention relates to image processing, specifically to methods for analyzing pixel neighborhoods in digital images. The problem addressed is accurately identifying and processing neighboring pixels around a target pixel to improve image analysis tasks such as noise reduction, edge detection, or feature extraction. Traditional approaches may struggle with inconsistent or inefficient pixel selection, leading to suboptimal results. The apparatus includes a processor configured to identify neighboring pixels of a target pixel in a digital image. The neighboring pixels are defined as other pixels within a predefined region centered on the target pixel. This region can be a square, circular, or other geometric shape, with the target pixel at its center. The processor further processes these neighboring pixels to perform operations such as filtering, interpolation, or pattern recognition. The apparatus may also include memory to store image data and a display for visualizing processed results. The system ensures that pixel neighborhoods are consistently and accurately defined, improving the reliability of subsequent image processing tasks. The invention is particularly useful in applications requiring precise local analysis, such as medical imaging, computer vision, or remote sensing.
16. The apparatus according to claim 10, wherein the processor is further configured to: determine a weighted sum of the initial brightness value of the target pixel and the initial brightness values of the neighboring pixels; and determine the weighted sum as the filtered brightness value of the target pixel.
This invention relates to image processing, specifically to a method for filtering pixel brightness values in an image to reduce noise or enhance visual quality. The problem addressed is the presence of noise or unwanted variations in pixel brightness, which can degrade image clarity. The apparatus includes a processor configured to analyze and modify pixel brightness values in an image. The processor identifies a target pixel and its neighboring pixels, then calculates a weighted sum of the initial brightness values of these pixels. The weights applied to each pixel's brightness value are determined based on their spatial relationship to the target pixel, with closer pixels typically receiving higher weights. The weighted sum is then used as the filtered brightness value of the target pixel, effectively smoothing or refining the brightness distribution in the image. This approach helps reduce noise while preserving important image details. The processor may also perform additional preprocessing steps, such as converting the image to a brightness domain, to facilitate accurate brightness value calculations. The invention is particularly useful in applications requiring high-quality image processing, such as medical imaging, photography, or video enhancement.
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January 10, 2022
May 14, 2024
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